Analysis of Intraday Volatility using High Frequency Data: An Empirical Investigation of Indian Futures Market

Shakeel, Moonis; Srivastava, Bhavana

doi:10.5958/2229-4503.2017.00014.5

Cited by 2 publications

(1 citation statement)

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“…Johnson, Jefferies, and Hui (2003) list as a stylized fact that the probability distribution of price changes displays non-trivial scaling properties. Shakeel and Srivastava (2017), used high frequency 30 minutes time interval data to empirically gauge the volatility behavior of Indian future market.…”

Section: Scalingmentioning

confidence: 99%

Stylized Facts of High-frequency Financial Time Series Data

Shakeel

Srivastava

2018

Global Business Review

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High-frequency financial time series data have an ability to define market microstructure and are helpful in making rational real-time decisions. These data sets carry unique characteristics and properties which are not available in low-frequency data; with that high-frequency data also create more challenges and opportunities for econometric modelling and financial data analysis. So it is essential to know the features and the facts related to the high-frequency time series data. In this article, we provide the characteristics and stylized facts exhibited by the high-frequency financial time series data of the S&P CNX Nifty futures index. Stylized facts are mostly related to the empirical observed behaviours, distributional properties, autocorrelation function and seasonality of the high-frequency data. Also, it illustrates the importance of stationarity in financial time series analysis. The knowledge of such facts and concepts is helpful to establish better empirical models and to produce reliable forecasts.

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Section: Scalingmentioning

confidence: 99%